Everything you want to know and never dared to ask. A practical approach to employing challenge-based learning in engineering ethics

Challenge-based learning (CBL) for engineering ethics tasks students with identifying ethical challenges in cooperation with an external partner, e.g., a technology company. As many best-practice parameters of such courses remain unclear, this contribution focuses on a teacher-centric introduction into deploying CBL for engineering ethics. Taking Goodlad’s curriculum typology as a point of departure, we discuss practical issues in devising, maintaining and evaluating CBL courses for engineering ethics both in terms of the temporal dimension (before, during and after the course) as well as in terms of the people involved. We will discuss selecting learning objectives, forms of knowledge acquisition, supporting self-organization, and fostering discursive etiquette, as well as cooperative, yet critical attitudes. Additionally, we will delve into strategic matters, e.g., ways to approach potential external partners and maintain fruitful cooperations

Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of harm, justice, privacy, and transparency are respected, medical machine learning systems must be developed responsibly. Many high-level declarations of ethical principles have been put forth for this purpose, but there is a severe lack of technical guidelines explicating the practical consequences for medical machine learning. Similarly, there is currently considerable uncertainty regarding the exact regulatory requirements placed upon medical machine learning systems. This survey provides an overview of the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges. First, a brief review of existing regulations affecting medical machine learning is provided, showing that properties such as safety, robustness, reliability, privacy, security, transparency, explainability, and nondiscrimination are all demanded already by existing law and regulations - albeit, in many cases, to an uncertain degree. Next, the key technical obstacles to achieving these desirable properties are discussed, as well as important techniques to overcome these obstacles in the medical context. We notice that distribution shift, spurious correlations, model underspecification, uncertainty quantification, and data scarcity represent severe challenges in the medical context. Promising solution approaches include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge, the use of inherently transparent models, comprehensive out-of-distribution model testing and verification, as well as algorithmic impact assessments